Monitoring Google Gemini API with SigNoz
Overview
This guide walks you through setting up monitoring for Google Gemini API using OpenTelemetry and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe model performance, capture request/response details, and track system-level metrics in SigNoz, giving you real-time visibility into latency, error rates, and usage trends for your Gemini applications.
Instrumenting Gemini in your LLM applications with telemetry ensures full observability across your AI workflows, making it easier to debug issues, optimize performance, and understand user interactions. By leveraging SigNoz, you can analyze correlated traces, logs, and metrics in unified dashboards, configure alerts, and gain actionable insights to continuously improve reliability, responsiveness, and user experience.
Prerequisites
- A SigNoz Cloud account with an active ingestion key
- Internet access to send telemetry data to SigNoz Cloud
- A Google Gemini API account with a working API Key
pipinstalled for managing Python packages- (Optional but recommended) A Python virtual environment to isolate dependencies
Monitoring Google Gemini
Step 1: Install the necessary packages in your Python environment.
pip install \
opentelemetry-api \
opentelemetry-sdk \
opentelemetry-exporter-otlp \
opentelemetry-instrumentation-system-metrics \
opentelemetry-instrumentation-httpx \
google-genai \
openinference-instrumentation-google-genai
Step 2: Import the necessary modules in your Python application
Traces:
from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor
from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
Logs:
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
from opentelemetry._logs import set_logger_provider
import logging
Metrics:
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor
Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry import trace
import os
resource = Resource.create({"service.name": "<service_name>"})
provider = TracerProvider(resource=resource)
span_exporter = OTLPSpanExporter(
endpoint= os.getenv("OTEL_EXPORTER_TRACES_ENDPOINT"),
headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
processor = BatchSpanProcessor(span_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
<service_name>is the name of your serviceOTEL_EXPORTER_TRACES_ENDPOINT→ SigNoz Cloud trace endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/tracesSIGNOZ_INGESTION_KEY→ Your SigNoz ingestion key
Step 4: Instrument Google Gemini using GoogleGenAIInstrumentor and the configured Tracer Provider
from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor
GoogleGenAIInstrumentor().instrument(tracer_provider=provider)
📌 Important: Place this code at the start of your application logic — before any Gemini functions are called or used — to ensure telemetry is correctly captured.
Step 5: Setup Logs
import logging
import os
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
logger_provider = LoggerProvider(resource=resource)
set_logger_provider(logger_provider)
otlp_log_exporter = OTLPLogExporter(
endpoint= os.getenv("OTEL_EXPORTER_LOGS_ENDPOINT"),
headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
logger_provider.add_log_record_processor(
BatchLogRecordProcessor(otlp_log_exporter)
)
# Attach OTel logging handler to root logger
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.basicConfig(level=logging.INFO, handlers=[handler])
logger = logging.getLogger(__name__)
<service_name>is the name of your serviceOTEL_EXPORTER_LOGS_ENDPOINT→ SigNoz Cloud logs endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/logsSIGNOZ_INGESTION_KEY→ Your SigNoz ingestion key
Step 6: Setup Metrics
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor
import os
resource = Resource.create({"service.name": "<service-name>"})
metric_exporter = OTLPMetricExporter(
endpoint= os.getenv("OTEL_EXPORTER_METRICS_ENDPOINT"),
headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
reader = PeriodicExportingMetricReader(metric_exporter)
metric_provider = MeterProvider(metric_readers=[reader], resource=resource)
metrics.set_meter_provider(metric_provider)
meter = metrics.get_meter(__name__)
# turn on out-of-the-box metrics
SystemMetricsInstrumentor().instrument()
HTTPXClientInstrumentor().instrument()
<service_name>is the name of your serviceOTEL_EXPORTER_METRICS_ENDPOINT→ SigNoz Cloud metrics endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/metricsSIGNOZ_INGESTION_KEY→ Your SigNoz ingestion key
📌 Note: SystemMetricsInstrumentor provides system metrics (CPU, memory, etc.), and HTTPXClientInstrumentor provides outbound HTTP request metrics such as request duration. These are not Gemini-specific metrics. Gemini does not expose metrics as part of their SDK. If you want to add custom metrics to your Gemini application, see Python Custom Metrics.
Step 7: Run an example
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-2.5-flash",
contents="What is SigNoz?",
)
print(response.text)
📌 Note: Ensure that the
GEMINI_API_KEYenvironment variable is properly defined with your API key before running the code.
View Traces, Logs, and Metrics in SigNoz
Your Google Gemini commands should now automatically emit traces, logs, and metrics.
You should be able to view traces in Signoz Cloud under the traces tab:
When you click on a trace in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes.
You can view logs by clicking on the “Related Logs” button in the trace view to see correlated logs for a given trace:
You should also be able to view logs in Signoz Cloud under the logs tab:
When you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:
You should be able to see Gemini metrics in Signoz Cloud under the metrics tab:
When you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:
Dashboard
You can also check out our custom Google Gemini dashboard here which provides specialized visualizations for monitoring your Gemini API usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.
Last updated: September 5, 2025
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